BigQuery DTS新增Iceberg与MCP服务器等连接器能力

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In a fast-paced digital economy, data is your most critical engine. Yet, many enterprises find themselves trapped in a costly paradox, spending over 100 hours a week building and fixing fragile, in-house ETL pipelines or wrestling with unpredictable third-party tools.

Trusted by thousands of customers every single day, BigQuery Data Transfer Service (DTS) eliminates this engineering burden. As a fully managed, zero-code data movement solution, BigQuery DTS automates data ingestion into BigQuery allowing your teams to transition from pipeline maintenance to strategic data science in minutes.

Expanding the ecosystem: New connectors and capabilities

We are rapidly expanding our integration landscape to eliminate data silos across databases, ads and marketing platforms. Here are the latest additions and enhancements

Open Lakehouse ingestion

• Direct ingestion into Apache Iceberg managed tables (Preview): You can now ingest data from common sources such as Google Cloud Storage, Amazon S3, and Azure Blob Storage directly into Iceberg managed tables. This enables you to maintain full multi-cloud storage cross-compatibility with other query engines while leveraging BigQuery's top-tier performance tuning.

Next-gen agentic architecture

• Fully managed remote Model Context Protocol (MCP) Server (Preview): This allows your developers to connect their AI application and agents to DTS allowing them to programmatically discover data sources and configure/execute transfers on the user's behalf.

Enterprise and relational databases (supports both full or incremental transfers)

• Microsoft SQL Server (Preview): Easily centralize transactional tables, schemas, and operational data directly into your analytical environment in BigQuery.
• PostgreSQL(GA) and MySQL (GA): Automates data delivery and simplifies the replication of high-volume web and application workloads into your central data warehouse within minutes. Supports data replication from on-premise environments, CloudSQL, and other clouds.

E-commerce and growth marketing

• Shopify(Preview): Automates the extraction of granular order histories, inventory logs, and customer profiles straight into your analytical schema.
• Klaviyo(Preview): Extracts detailed email and SMS engagement logs (such as clicks, sends, and opens) to build precise multi-channel lifecycle attributes.
• HubSpot(Preview) : Syncs pipeline, contact tracking, and inbound marketing metrics to keep your revenue operations teams aligned.
• Mailchimp(Preview): Automatically moves campaign performances and audience list attributes directly into your data warehouse.

Migration connectors

• Snowflake(GA): Migrate your data from Snowflake with features like incremental transfer, auto schema detection, private connectivity and support for migrating data residing on all three major clouds (Google Cloud, AWS, and Azure)

Enhancement to major connectors

• ServiceNow, Salesforce, and Oracle: Enhanced with native incremental update support to speed up large-scale pipeline refreshes for enterprise CRM, ITSM, and financial workflows.

Why you should choose BigQuery Data Transfer Service

Moving data across an enterprise architecture shouldn't require complex compromises between cost, management overhead, and pipeline health. BigQuery DTS delivers unique advantages across three pillars:

1. Unbeatable cost efficiency

• Zero Ingestion Costs for major sources: Data ingestion at no-charge for all first-party Google sources (except Google Play), including Google Ads, Google Analytics 4, Campaign Manager, YouTube and Google Cloud Storage. Ingestion cost are also free for Amazon S3, Azure Blob Storage, Amazon Redshift, and Teradata
• Low consumption-based rates for third-party SaaS: Ingestion from 3rd-party sources are entirely on a flexible consumption model. Compute fees run less than 6 cents per slot-hour in major regions. Because pricing scales with compute footprint rather than row volume, depending on your source data compression format and available network bandwidth, you can efficiently transfer massive data volumes.

2. Frictionless security and native management

Eliminate middlemen servers and external security and API configurations. BigQuery DTS fully integrates with Cloud IAM. Data transfers instantly inherit your destination dataset’s Column-Level Security, Row-Level Security, and Customer-Managed Encryption Keys (CMEK) without extra configuration overhead. Your streams flow securely inside the native Google Cloud perimeter.

3. Industry-leading performance and resilience

When you manage data at enterprise scale, downtime means lost business. BigQuery DTS provides a highly resilient ingestion footprint backed by a strict Google Cloud Service Level Agreement (SLA). The system delivers a monthly uptime percentage of >=99.99%, guaranteeing your analytics, automated pipelines, and operational dashboards update reliably.

Ready to transform your data operations?

Stop letting manual ingestion scripts limit your growth. Join the thousands of companies relying on Google's native cloud lakehouse data movement architecture to build a modern, scalable data stack.

Try the platform out today: Navigate directly to the BigQuery Data Transfer Service console, pick your connector, and deploy your first automated transfer in just a few clicks!

What connectors should we build next?

We are constantly expanding our native integration library based on your business needs. What sources are you currently forced to extract manually? Are there specific relational databases, NoSQL engines, or regional SaaS platforms you need to replicate next? Let us know with a feature request.

Please file feature requests via the public issue tracker.

Posted in

• Data Analytics
• BigQuery